Circle Detection Using Morphological Operations

Circle detection is the very important and challenging field in image processing. In previous few decants the field of fining and detection of circular objects in images gain more attention because the location and additional information of circular object in the image can easily be find when a circle is extracted in that image so that information can be used in industries and businesses in many ways. The circle detection is today commonly used in computer application i.e. Computer vision applications and in the field of robotics to detect and recognize the circular objects. In measurement based images the circle detection is the most important task and necessary task. In my article I used a method that detect circle in the image with fast calculations and in short time instant of these methods that perform heavy calculations that consume processing power of GPU as well as time in circle detection. This method uses a simple algorithm that detect circle using simple calculation

Automated Food Ordering System

Online food order system is a site planned essentially for use in the nourishment conveyance industry. This framework will enable inns and eateries to expand extent of business by decreasing the work cost included. The framework likewise permits to rapidly and effortlessly deal with an online menu which clients can peruse and use to put orders with only couple of snaps. Eatery workers at that point utilize these requests through a simple to explore graphical interface for proficient preparing

Flower Detection in Digital Image Processing using Global Image Enhancement Method

Flowers are plays an important role on the planet as they contain the reproduction part of plants. The only flower parts of plants have ability to produce different kinds of fruit, vegetable, and seeds for humans. Flower seeds also are used to produce oil. Honey bees collect nectar from flowers to produce honey. In this paper a new approach is proposed for flower detection. The proposed algorithm is use Global image enhancement and thresholdging technique for flower detection in digital images

Time Complexity of Color Camera Depth Map Hand Edge Closing Recognition Algorithm

The objective of this paper is to calculate the time complexity of the colored camera depth map hand edge closing algorithm of the hand gesture recognition technique. It has been identified as hand gesture recognition through human-computer interaction using color camera and depth map technique, which is used to find the time complexity of the algorithms using 2D minima methods, brute force, and plane sweep. Human-computer interaction is a very much essential component of most people's daily life. The goal of gesture recognition research is to establish a system that can classify specific human gestures and can make its use to convey information for the device control. These methods have different input types and different classifiers and techniques to identify hand gestures. This paper includes the algorithm of one of the hand gesture recognition “Color camera depth map hand edge recognition” algorithm and its time complexity and simulation on MATLAB

Reducing the environmental impact of surgery on a global scale: systematic review and co-prioritization with healthcare workers in 132 countries

Abstract Background Healthcare cannot achieve net-zero carbon without addressing operating theatres. The aim of this study was to prioritize feasible interventions to reduce the environmental impact of operating theatres. Methods This study adopted a four-phase Delphi consensus co-prioritization methodology. In phase 1, a systematic review of published interventions and global consultation of perioperative healthcare professionals were used to longlist interventions. In phase 2, iterative thematic analysis consolidated comparable interventions into a shortlist. In phase 3, the shortlist was co-prioritized based on patient and clinician views on acceptability, feasibility, and safety. In phase 4, ranked lists of interventions were presented by their relevance to high-income countries and low–middle-income countries. Results In phase 1, 43 interventions were identified, which had low uptake in practice according to 3042 professionals globally. In phase 2, a shortlist of 15 intervention domains was generated. In phase 3, interventions were deemed acceptable for more than 90 per cent of patients except for reducing general anaesthesia (84 per cent) and re-sterilization of ‘single-use’ consumables (86 per cent). In phase 4, the top three shortlisted interventions for high-income countries were: introducing recycling; reducing use of anaesthetic gases; and appropriate clinical waste processing. In phase 4, the top three shortlisted interventions for low–middle-income countries were: introducing reusable surgical devices; reducing use of consumables; and reducing the use of general anaesthesia. Conclusion This is a step toward environmentally sustainable operating environments with actionable interventions applicable to both high– and low–middle–income countries

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

To save lives from cancer, it is very crucial to diagnose it at its early stages. One solution to early diagnosis lies in the identification of the cancer driver genes and their mutations. Such diagnostics can substantially minimize the mortality rate of this deadly disease. However, concurrently, the identification of cancer driver gene mutation through experimental mechanisms could be an expensive, slow, and laborious job. The advancement of computational strategies that could help in the early prediction of cancer growth effectively and accurately is thus highly needed towards early diagnoses and a decrease in the mortality rates due to this disease. Herein, we aim to predict clear cell renal carcinoma (RCCC) at the level of the genes, using the genomic sequences. The dataset was taken from IntOgen Cancer Mutations Browser and all genes’ standard DNA sequences were taken from the NCBI database. Using cancer-associated information of mutation from INTOGEN, the benchmark dataset was generated by creating the mutations in original sequences. After extensive feature extraction, the dataset was used to train ANN+ Hist Gradient boosting that could perform the classification of RCCC genes, other cancer-associated genes, and non-cancerous/unknown (non-tumor driver) genes. Through an independent dataset test, the accuracy observed was 83%, whereas the 10-fold cross-validation and Jackknife validation yielded 98% and 100% accurate results, respectively. The proposed predictor RCCC_Pred is able to identify RCCC genes with high accuracy and efficiency and can help scientists/researchers easily predict and diagnose cancer at its early stages

RCCC_Pred: A Novel Method for Sequence-Based Identification of Renal Clear Cell Carcinoma Genes through DNA Mutations and a Blend of Features

To save lives from cancer, it is very crucial to diagnose it at its early stages. One solution to early diagnosis lies in the identification of the cancer driver genes and their mutations. Such diagnostics can substantially minimize the mortality rate of this deadly disease. However, concurrently, the identification of cancer driver gene mutation through experimental mechanisms could be an expensive, slow, and laborious job. The advancement of computational strategies that could help in the early prediction of cancer growth effectively and accurately is thus highly needed towards early diagnoses and a decrease in the mortality rates due to this disease. Herein, we aim to predict clear cell renal carcinoma (RCCC) at the level of the genes, using the genomic sequences. The dataset was taken from IntOgen Cancer Mutations Browser and all genes’ standard DNA sequences were taken from the NCBI database. Using cancer-associated information of mutation from INTOGEN, the benchmark dataset was generated by creating the mutations in original sequences. After extensive feature extraction, the dataset was used to train ANN+ Hist Gradient boosting that could perform the classification of RCCC genes, other cancer-associated genes, and non-cancerous/unknown (non-tumor driver) genes. Through an independent dataset test, the accuracy observed was 83%, whereas the 10-fold cross-validation and Jackknife validation yielded 98% and 100% accurate results, respectively. The proposed predictor RCCC_Pred is able to identify RCCC genes with high accuracy and efficiency and can help scientists/researchers easily predict and diagnose cancer at its early stages

Image Segmentation by Using Threshold Techniques

Image segmentation (IS) is a procedure by which provided picture can be subdivided into many segments and to observe every segment included in the image. The desired result could be searched by observing them and that information we get is helpful for high standard machine vision software. The difficulties of IS has large problems for computer vision. Many procedures which came under IS are Edge based IS (EBIS), Region based IS (RBIS), Threshold based IS (TBIS). The result of observing image which is relying upon the reliability of IS, but exact division of a picture is most difficult issue. The technique we are using in this article is thresholding based segmentation (TBS). The studied article of IS by reader is beneficial for analyzing the suitable IS techniques and also for improvement of efficiency, performance and major goal, that helps in building latest algorithms

Automation of Blood Cancer Risk Analysis Using Fuzzy Logic

The statistical estimation of the cancer research center shows that approximate 23 million new cases were recorded in 2010 in all over the world. Blood cancer is a deadly disease and have different categories. Symptom of every category is different, but some initial symptoms are same. Research and statistics show that in the majority of cases, people know about the disease when it became too late. Fuzzy is being used in plentiful field like engineering, data mining, medical, science and Computational computing. This research work proposes a method which can be able to assist the doctor in diagnosis of blood Cancer. Fuzzy introduce varies inference but famous fuzzy inference types are Mamdani and TSK. In this article Mamdani Inference System has been proposed to measure the Blood Cancer Detector (BCD). The proposed method Fuzzy Logic based Blood Cancer Detector (BCD) is taking 10 input parameters and 1 output variable. The input variables are BI (Blood Infection), Bleeding, Pains, SC (skin changes), AD (Abdominal Discomforts), VP (Vision problem), WL (weight loss), BP (Bladder problems), fatigue & TP (throat problems), and output variable is BCD

Diagnosis of endometriosis in the light of prevalent theories

Endometriosis is a gynecological condition recognized by the existence of ectopic endometrial tissue outside the uterus. It is predominantly present in females of reproductive age group and is one of the main causes of infertility. Even with a predictable prevalence of 11% in females and considerable historical explanations adopted from the seventeenth century, the diagnosis of endometriosis still remains doubtful. The conventional concepts on histological basis of endometriosis are explained by a number of theories. Medical signs of endometriosis contain prolonged pelvic ache, dyspareunia, repeated menstrual discomfort and chronic pelvic pain which can severely affect the excellence of life and health of the patient. In this review we will discuss the prevalent theories for the diagnosis of endometriosis and suggestions to identify the condition well in time for better control and management